AI Based Discovery of a New AKR1C3 Inhibitor for Anticancer Applications

Area of Science:

• Biochemistry
• Oncology
• Pharmacology

Background:

• AKR1C3 enzyme is upregulated in various cancers, influencing hormone synthesis, tumor aggressiveness, and drug resistance.
• Targeting AKR1C3 is a potential strategy for cancer therapy.

Purpose of the Study:

• To discover novel, potent AKR1C3 inhibitors using an unbiased, AI-driven approach.
• To evaluate the antiproliferative and synergistic effects of identified compounds in cancer models.

Main Methods:

• AI-based virtual drug screening to identify potential AKR1C3 inhibitors.
• In vitro enzymatic assays to confirm inhibitor potency and selectivity.
• Antiproliferative assays using prostate cancer cell lines (22RV1).
• Combination studies with established chemotherapeutics (abiraterone, doxorubicin) in cancer cell lines.

Main Results:

• Compound 4 was identified as a potent and selective AKR1C3 inhibitor via AI screening.
• Compound 4 demonstrated significant antiproliferative effects in the 22RV1 prostate cancer model.
• Compound 4 enhanced the activity of abiraterone in prostate cancer and showed synergistic effects with doxorubicin in osteosarcoma, correlated with AKR1C3 expression.

Conclusions:

• AI is effective in discovering novel enzyme inhibitors like compound 4.
• Compound 4 is a promising AKR1C3 inhibitor with potential to improve existing cancer therapies.
• The findings support AKR1C3 as a therapeutic target and highlight compound 4's potential in combination chemotherapy.